{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "c2_paper_df = pd.read_csv(\"../csvs/c2x2-paper-d200-t1.csv\")\n",
    "c2_zg_df = pd.read_csv(\"../csvs/c2x2-zero-goal-d200-t1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_dt</th>\n",
       "      <th>stop_dt</th>\n",
       "      <th>duration</th>\n",
       "      <th>depth</th>\n",
       "      <th>scramble</th>\n",
       "      <th>is_solved</th>\n",
       "      <th>solve_steps</th>\n",
       "      <th>solution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-11-03T07:28:22.799230</td>\n",
       "      <td>2018-11-03T07:28:22.807523</td>\n",
       "      <td>0.008293</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-11-03T07:28:22.807541</td>\n",
       "      <td>2018-11-03T07:28:22.815057</td>\n",
       "      <td>0.007516</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-11-03T07:28:22.815078</td>\n",
       "      <td>2018-11-03T07:28:22.820461</td>\n",
       "      <td>0.005383</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-11-03T07:28:22.820480</td>\n",
       "      <td>2018-11-03T07:28:22.826494</td>\n",
       "      <td>0.006014</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-11-03T07:28:22.826512</td>\n",
       "      <td>2018-11-03T07:28:22.831506</td>\n",
       "      <td>0.004994</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     start_dt                     stop_dt  duration  depth  \\\n",
       "0  2018-11-03T07:28:22.799230  2018-11-03T07:28:22.807523  0.008293      1   \n",
       "1  2018-11-03T07:28:22.807541  2018-11-03T07:28:22.815057  0.007516      1   \n",
       "2  2018-11-03T07:28:22.815078  2018-11-03T07:28:22.820461  0.005383      1   \n",
       "3  2018-11-03T07:28:22.820480  2018-11-03T07:28:22.826494  0.006014      1   \n",
       "4  2018-11-03T07:28:22.826512  2018-11-03T07:28:22.831506  0.004994      1   \n",
       "\n",
       "  scramble  is_solved  solve_steps  solution  \n",
       "0       10          1            0       NaN  \n",
       "1        1          1            0       NaN  \n",
       "2        0          1            0       NaN  \n",
       "3       11          1            0       NaN  \n",
       "4        4          1            0       NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c2_paper_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot('depth', 'duration', data=c2_paper_df);\n",
    "sns.lineplot('depth', 'duration', data=c2_zg_df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot('depth', 'is_solved', data=c2_paper_df);\n",
    "sns.lineplot('depth', 'is_solved', data=c2_zg_df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot('depth', 'solve_steps', data=c2_paper_df);\n",
    "sns.lineplot('depth', 'solve_steps', data=c2_zg_df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot('depth', 'solve_steps', data=c2_paper_df[c2_paper_df.is_solved == 1]);\n",
    "sns.lineplot('depth', 'solve_steps', data=c2_zg_df[c2_zg_df.is_solved == 1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot('depth', 'solve_steps', data=c2_paper_df[c2_paper_df.is_solved == 0]);\n",
    "sns.lineplot('depth', 'solve_steps', data=c2_zg_df[c2_zg_df.is_solved == 0]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shmuma/anaconda3/envs/art_01_cube/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot('depth', 'solve_steps', data=c2_zg_df[c2_zg_df.is_solved == 1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_dt</th>\n",
       "      <th>stop_dt</th>\n",
       "      <th>duration</th>\n",
       "      <th>depth</th>\n",
       "      <th>scramble</th>\n",
       "      <th>is_solved</th>\n",
       "      <th>solve_steps</th>\n",
       "      <th>solution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2018-11-03T07:28:27.937404</td>\n",
       "      <td>2018-11-03T07:29:27.965407</td>\n",
       "      <td>60.028003</td>\n",
       "      <td>4</td>\n",
       "      <td>2 10 1 4</td>\n",
       "      <td>0</td>\n",
       "      <td>4370</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2018-11-03T07:29:41.432092</td>\n",
       "      <td>2018-11-03T07:30:41.465964</td>\n",
       "      <td>60.033872</td>\n",
       "      <td>5</td>\n",
       "      <td>7 5 9 0 11</td>\n",
       "      <td>0</td>\n",
       "      <td>5679</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>2018-11-03T07:31:44.788987</td>\n",
       "      <td>2018-11-03T07:32:44.825293</td>\n",
       "      <td>60.036306</td>\n",
       "      <td>5</td>\n",
       "      <td>5 5 9 11 6</td>\n",
       "      <td>0</td>\n",
       "      <td>4156</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2018-11-03T07:32:46.300011</td>\n",
       "      <td>2018-11-03T07:33:46.318301</td>\n",
       "      <td>60.018290</td>\n",
       "      <td>5</td>\n",
       "      <td>1 1 10 11 6</td>\n",
       "      <td>0</td>\n",
       "      <td>3547</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2018-11-03T07:33:47.996212</td>\n",
       "      <td>2018-11-03T07:34:48.031956</td>\n",
       "      <td>60.035744</td>\n",
       "      <td>5</td>\n",
       "      <td>2 4 4 2 1</td>\n",
       "      <td>0</td>\n",
       "      <td>3308</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2018-11-03T07:35:23.475751</td>\n",
       "      <td>2018-11-03T07:36:23.491487</td>\n",
       "      <td>60.015736</td>\n",
       "      <td>6</td>\n",
       "      <td>11 10 9 5 7 8</td>\n",
       "      <td>0</td>\n",
       "      <td>3640</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>2018-11-03T07:37:27.455060</td>\n",
       "      <td>2018-11-03T07:38:27.543656</td>\n",
       "      <td>60.088596</td>\n",
       "      <td>6</td>\n",
       "      <td>4 9 0 5 3 10</td>\n",
       "      <td>0</td>\n",
       "      <td>4584</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>2018-11-03T07:39:32.422113</td>\n",
       "      <td>2018-11-03T07:40:32.458444</td>\n",
       "      <td>60.036331</td>\n",
       "      <td>7</td>\n",
       "      <td>1 0 10 2 6 3 10</td>\n",
       "      <td>0</td>\n",
       "      <td>4292</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2018-11-03T07:40:36.349231</td>\n",
       "      <td>2018-11-03T07:41:36.376140</td>\n",
       "      <td>60.026909</td>\n",
       "      <td>7</td>\n",
       "      <td>2 3 4 2 6 4 4</td>\n",
       "      <td>0</td>\n",
       "      <td>2922</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2018-11-03T07:41:51.924902</td>\n",
       "      <td>2018-11-03T07:42:52.005646</td>\n",
       "      <td>60.080744</td>\n",
       "      <td>7</td>\n",
       "      <td>8 4 8 1 8 10 7</td>\n",
       "      <td>0</td>\n",
       "      <td>3467</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>2018-11-03T07:43:19.376918</td>\n",
       "      <td>2018-11-03T07:44:19.414716</td>\n",
       "      <td>60.037798</td>\n",
       "      <td>7</td>\n",
       "      <td>5 3 3 7 6 8 4</td>\n",
       "      <td>0</td>\n",
       "      <td>3962</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2018-11-03T07:44:24.980411</td>\n",
       "      <td>2018-11-03T07:45:25.019995</td>\n",
       "      <td>60.039584</td>\n",
       "      <td>8</td>\n",
       "      <td>5 7 7 9 9 1 1 10</td>\n",
       "      <td>0</td>\n",
       "      <td>3375</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>2018-11-03T07:45:34.331774</td>\n",
       "      <td>2018-11-03T07:46:34.388275</td>\n",
       "      <td>60.056501</td>\n",
       "      <td>8</td>\n",
       "      <td>7 3 6 10 6 3 10 11</td>\n",
       "      <td>0</td>\n",
       "      <td>3532</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>2018-11-03T07:46:34.388370</td>\n",
       "      <td>2018-11-03T07:47:34.486795</td>\n",
       "      <td>60.098425</td>\n",
       "      <td>8</td>\n",
       "      <td>9 0 3 0 8 1 9 6</td>\n",
       "      <td>0</td>\n",
       "      <td>6218</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>2018-11-03T07:47:34.496008</td>\n",
       "      <td>2018-11-03T07:48:34.526353</td>\n",
       "      <td>60.030345</td>\n",
       "      <td>8</td>\n",
       "      <td>3 8 11 7 6 2 1 5</td>\n",
       "      <td>0</td>\n",
       "      <td>2677</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>2018-11-03T07:48:34.526447</td>\n",
       "      <td>2018-11-03T07:49:34.570087</td>\n",
       "      <td>60.043640</td>\n",
       "      <td>8</td>\n",
       "      <td>11 2 0 9 5 10 9 10</td>\n",
       "      <td>0</td>\n",
       "      <td>4772</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>2018-11-03T07:49:34.570239</td>\n",
       "      <td>2018-11-03T07:50:34.607524</td>\n",
       "      <td>60.037285</td>\n",
       "      <td>8</td>\n",
       "      <td>3 5 1 6 2 6 9 11</td>\n",
       "      <td>0</td>\n",
       "      <td>5521</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>2018-11-03T07:50:34.607674</td>\n",
       "      <td>2018-11-03T07:51:34.660917</td>\n",
       "      <td>60.053243</td>\n",
       "      <td>8</td>\n",
       "      <td>2 6 7 0 10 2 10 7</td>\n",
       "      <td>0</td>\n",
       "      <td>3314</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>2018-11-03T07:52:07.618923</td>\n",
       "      <td>2018-11-03T07:53:07.652775</td>\n",
       "      <td>60.033852</td>\n",
       "      <td>8</td>\n",
       "      <td>7 5 6 10 6 5 7 3</td>\n",
       "      <td>0</td>\n",
       "      <td>7066</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>2018-11-03T07:53:07.652922</td>\n",
       "      <td>2018-11-03T07:54:07.670170</td>\n",
       "      <td>60.017248</td>\n",
       "      <td>8</td>\n",
       "      <td>6 4 5 2 6 10 5 8</td>\n",
       "      <td>0</td>\n",
       "      <td>2474</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>2018-11-03T07:54:07.670324</td>\n",
       "      <td>2018-11-03T07:55:07.733730</td>\n",
       "      <td>60.063406</td>\n",
       "      <td>8</td>\n",
       "      <td>4 7 8 7 0 7 10 10</td>\n",
       "      <td>0</td>\n",
       "      <td>5105</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>2018-11-03T07:55:07.733825</td>\n",
       "      <td>2018-11-03T07:56:07.757373</td>\n",
       "      <td>60.023548</td>\n",
       "      <td>8</td>\n",
       "      <td>0 4 8 5 1 1 9 9</td>\n",
       "      <td>0</td>\n",
       "      <td>5448</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>2018-11-03T07:56:07.757523</td>\n",
       "      <td>2018-11-03T07:57:07.833445</td>\n",
       "      <td>60.075922</td>\n",
       "      <td>8</td>\n",
       "      <td>9 0 0 4 9 6 3 0</td>\n",
       "      <td>0</td>\n",
       "      <td>6369</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>2018-11-03T07:57:13.646697</td>\n",
       "      <td>2018-11-03T07:58:13.698956</td>\n",
       "      <td>60.052259</td>\n",
       "      <td>9</td>\n",
       "      <td>3 0 11 3 4 11 11 7 11</td>\n",
       "      <td>0</td>\n",
       "      <td>4974</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>2018-11-03T07:58:32.027507</td>\n",
       "      <td>2018-11-03T07:59:32.134805</td>\n",
       "      <td>60.107298</td>\n",
       "      <td>9</td>\n",
       "      <td>1 5 4 2 11 9 11 10 9</td>\n",
       "      <td>0</td>\n",
       "      <td>8715</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>2018-11-03T07:59:32.134898</td>\n",
       "      <td>2018-11-03T08:00:32.169663</td>\n",
       "      <td>60.034765</td>\n",
       "      <td>9</td>\n",
       "      <td>2 3 4 1 0 10 11 2 4</td>\n",
       "      <td>0</td>\n",
       "      <td>7278</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>2018-11-03T08:00:32.169809</td>\n",
       "      <td>2018-11-03T08:01:32.273574</td>\n",
       "      <td>60.103765</td>\n",
       "      <td>9</td>\n",
       "      <td>11 1 2 2 0 9 4 3 4</td>\n",
       "      <td>0</td>\n",
       "      <td>13498</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>2018-11-03T08:01:32.273671</td>\n",
       "      <td>2018-11-03T08:02:32.371686</td>\n",
       "      <td>60.098015</td>\n",
       "      <td>9</td>\n",
       "      <td>10 2 1 10 3 1 11 9 1</td>\n",
       "      <td>0</td>\n",
       "      <td>3751</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>2018-11-03T08:02:32.371780</td>\n",
       "      <td>2018-11-03T08:03:32.392502</td>\n",
       "      <td>60.020722</td>\n",
       "      <td>9</td>\n",
       "      <td>9 4 4 7 4 0 1 10 11</td>\n",
       "      <td>0</td>\n",
       "      <td>4618</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>2018-11-03T08:03:32.392652</td>\n",
       "      <td>2018-11-03T08:04:32.452510</td>\n",
       "      <td>60.059858</td>\n",
       "      <td>9</td>\n",
       "      <td>9 7 3 11 3 11 10 2 4</td>\n",
       "      <td>0</td>\n",
       "      <td>5656</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>2018-11-03T10:17:59.527796</td>\n",
       "      <td>2018-11-03T10:18:59.587762</td>\n",
       "      <td>60.059966</td>\n",
       "      <td>18</td>\n",
       "      <td>1 9 4 4 9 11 8 4 7 3 10 1 11 4 0 2 4 6</td>\n",
       "      <td>0</td>\n",
       "      <td>7329</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>2018-11-03T10:18:59.657119</td>\n",
       "      <td>2018-11-03T10:19:59.707851</td>\n",
       "      <td>60.050732</td>\n",
       "      <td>19</td>\n",
       "      <td>9 10 1 3 1 9 9 5 7 3 1 11 8 1 4 3 1 10 6</td>\n",
       "      <td>0</td>\n",
       "      <td>9645</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>2018-11-03T10:19:59.707995</td>\n",
       "      <td>2018-11-03T10:20:59.753776</td>\n",
       "      <td>60.045781</td>\n",
       "      <td>19</td>\n",
       "      <td>6 5 7 4 11 7 0 4 7 11 3 11 8 5 1 6 4 7 4</td>\n",
       "      <td>0</td>\n",
       "      <td>5430</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2018-11-03T10:20:59.753916</td>\n",
       "      <td>2018-11-03T10:21:59.791365</td>\n",
       "      <td>60.037449</td>\n",
       "      <td>19</td>\n",
       "      <td>8 4 1 11 10 9 5 2 11 3 7 3 11 0 0 5 1 9 10</td>\n",
       "      <td>0</td>\n",
       "      <td>5389</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>2018-11-03T10:21:59.791506</td>\n",
       "      <td>2018-11-03T10:22:59.839420</td>\n",
       "      <td>60.047914</td>\n",
       "      <td>19</td>\n",
       "      <td>9 2 5 5 10 6 6 3 1 2 1 9 6 4 8 3 5 3 1</td>\n",
       "      <td>0</td>\n",
       "      <td>3349</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>2018-11-03T10:22:59.839563</td>\n",
       "      <td>2018-11-03T10:23:59.867941</td>\n",
       "      <td>60.028378</td>\n",
       "      <td>19</td>\n",
       "      <td>9 11 7 9 6 4 5 6 10 0 0 2 2 1 1 9 4 1 3</td>\n",
       "      <td>0</td>\n",
       "      <td>5281</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>2018-11-03T10:23:59.868091</td>\n",
       "      <td>2018-11-03T10:24:59.941758</td>\n",
       "      <td>60.073667</td>\n",
       "      <td>19</td>\n",
       "      <td>4 11 0 5 5 10 5 8 7 5 0 5 1 8 4 7 11 4 8</td>\n",
       "      <td>0</td>\n",
       "      <td>7189</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>2018-11-03T10:25:03.972281</td>\n",
       "      <td>2018-11-03T10:26:03.995328</td>\n",
       "      <td>60.023047</td>\n",
       "      <td>19</td>\n",
       "      <td>2 0 8 10 3 4 1 5 10 3 10 6 11 8 5 3 6 5 4</td>\n",
       "      <td>0</td>\n",
       "      <td>4411</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>2018-11-03T10:26:03.995468</td>\n",
       "      <td>2018-11-03T10:27:04.016416</td>\n",
       "      <td>60.020948</td>\n",
       "      <td>19</td>\n",
       "      <td>1 6 10 8 7 6 11 10 11 10 0 0 2 6 5 8 7 6 2</td>\n",
       "      <td>0</td>\n",
       "      <td>4661</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>2018-11-03T10:27:04.016551</td>\n",
       "      <td>2018-11-03T10:28:04.067421</td>\n",
       "      <td>60.050870</td>\n",
       "      <td>19</td>\n",
       "      <td>11 6 10 9 5 2 1 1 9 1 4 9 6 3 0 0 5 8 6</td>\n",
       "      <td>0</td>\n",
       "      <td>3937</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>2018-11-03T10:28:04.067581</td>\n",
       "      <td>2018-11-03T10:29:04.104962</td>\n",
       "      <td>60.037381</td>\n",
       "      <td>19</td>\n",
       "      <td>5 6 3 2 3 4 8 5 8 5 1 11 11 4 9 2 6 11 2</td>\n",
       "      <td>0</td>\n",
       "      <td>3249</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>2018-11-03T10:29:04.105105</td>\n",
       "      <td>2018-11-03T10:30:04.137153</td>\n",
       "      <td>60.032048</td>\n",
       "      <td>19</td>\n",
       "      <td>11 0 11 0 1 1 6 1 9 1 3 0 3 11 4 6 2 2 7</td>\n",
       "      <td>0</td>\n",
       "      <td>6841</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>2018-11-03T10:31:22.442663</td>\n",
       "      <td>2018-11-03T10:32:22.479913</td>\n",
       "      <td>60.037250</td>\n",
       "      <td>19</td>\n",
       "      <td>0 0 4 11 10 0 1 1 11 6 3 7 0 9 8 9 0 5 10</td>\n",
       "      <td>0</td>\n",
       "      <td>3146</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>2018-11-03T10:32:22.480025</td>\n",
       "      <td>2018-11-03T10:33:22.511064</td>\n",
       "      <td>60.031039</td>\n",
       "      <td>19</td>\n",
       "      <td>9 6 6 6 6 2 1 9 1 8 1 2 3 8 1 2 3 8 10</td>\n",
       "      <td>0</td>\n",
       "      <td>5982</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>2018-11-03T10:33:22.511204</td>\n",
       "      <td>2018-11-03T10:34:22.552686</td>\n",
       "      <td>60.041482</td>\n",
       "      <td>19</td>\n",
       "      <td>5 1 3 4 3 6 2 2 4 1 1 6 5 5 4 1 6 1 11</td>\n",
       "      <td>0</td>\n",
       "      <td>8593</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>2018-11-03T10:34:22.552817</td>\n",
       "      <td>2018-11-03T10:35:22.608276</td>\n",
       "      <td>60.055459</td>\n",
       "      <td>19</td>\n",
       "      <td>5 0 10 3 10 5 8 7 4 5 1 4 1 10 9 9 11 8 5</td>\n",
       "      <td>0</td>\n",
       "      <td>7290</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>2018-11-03T10:35:31.497771</td>\n",
       "      <td>2018-11-03T10:36:31.525108</td>\n",
       "      <td>60.027337</td>\n",
       "      <td>20</td>\n",
       "      <td>8 1 3 7 8 3 0 7 8 0 3 4 0 10 10 11 7 9 4 4</td>\n",
       "      <td>0</td>\n",
       "      <td>4787</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>2018-11-03T10:36:31.525229</td>\n",
       "      <td>2018-11-03T10:37:31.559053</td>\n",
       "      <td>60.033824</td>\n",
       "      <td>20</td>\n",
       "      <td>9 11 8 10 6 4 0 2 4 11 11 4 2 7 0 9 4 1 8 4</td>\n",
       "      <td>0</td>\n",
       "      <td>6473</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>2018-11-03T10:37:31.559192</td>\n",
       "      <td>2018-11-03T10:38:31.621067</td>\n",
       "      <td>60.061875</td>\n",
       "      <td>20</td>\n",
       "      <td>2 9 5 6 7 9 2 2 10 7 9 5 4 4 6 8 11 6 4 5</td>\n",
       "      <td>0</td>\n",
       "      <td>11773</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>2018-11-03T10:38:31.621207</td>\n",
       "      <td>2018-11-03T10:39:31.666771</td>\n",
       "      <td>60.045564</td>\n",
       "      <td>20</td>\n",
       "      <td>10 3 1 11 10 7 2 10 2 4 4 8 6 3 11 9 9 7 10 1</td>\n",
       "      <td>0</td>\n",
       "      <td>8750</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>2018-11-03T10:39:31.666912</td>\n",
       "      <td>2018-11-03T10:40:31.704211</td>\n",
       "      <td>60.037299</td>\n",
       "      <td>20</td>\n",
       "      <td>7 2 7 3 7 9 9 11 2 5 2 4 5 6 7 11 3 8 4 1</td>\n",
       "      <td>0</td>\n",
       "      <td>5905</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>2018-11-03T10:40:31.704350</td>\n",
       "      <td>2018-11-03T10:41:31.746074</td>\n",
       "      <td>60.041724</td>\n",
       "      <td>20</td>\n",
       "      <td>5 4 5 9 0 1 1 10 7 11 0 2 5 6 2 6 11 2 0 9</td>\n",
       "      <td>0</td>\n",
       "      <td>6909</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>2018-11-03T10:41:31.746213</td>\n",
       "      <td>2018-11-03T10:42:31.776821</td>\n",
       "      <td>60.030608</td>\n",
       "      <td>20</td>\n",
       "      <td>9 1 6 9 11 8 9 5 0 9 4 1 2 2 3 7 9 11 4 4</td>\n",
       "      <td>0</td>\n",
       "      <td>3010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>2018-11-03T10:42:31.776909</td>\n",
       "      <td>2018-11-03T10:43:31.816193</td>\n",
       "      <td>60.039284</td>\n",
       "      <td>20</td>\n",
       "      <td>4 5 7 8 7 11 11 11 4 11 6 8 9 0 2 1 11 6 7 10</td>\n",
       "      <td>0</td>\n",
       "      <td>8145</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>2018-11-03T10:43:31.816280</td>\n",
       "      <td>2018-11-03T10:44:31.851691</td>\n",
       "      <td>60.035411</td>\n",
       "      <td>20</td>\n",
       "      <td>0 11 0 0 8 3 5 1 9 1 10 11 3 3 5 5 10 0 7 11</td>\n",
       "      <td>0</td>\n",
       "      <td>5160</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>2018-11-03T10:44:31.851775</td>\n",
       "      <td>2018-11-03T10:45:31.889726</td>\n",
       "      <td>60.037951</td>\n",
       "      <td>20</td>\n",
       "      <td>6 3 0 3 1 9 6 6 10 5 2 7 4 11 11 11 7 6 11 3</td>\n",
       "      <td>0</td>\n",
       "      <td>7564</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>2018-11-03T10:46:06.138408</td>\n",
       "      <td>2018-11-03T10:47:06.176277</td>\n",
       "      <td>60.037869</td>\n",
       "      <td>20</td>\n",
       "      <td>8 3 1 11 10 11 10 2 3 8 11 1 6 6 1 1 5 5 1 8</td>\n",
       "      <td>0</td>\n",
       "      <td>6524</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>2018-11-03T10:47:06.176363</td>\n",
       "      <td>2018-11-03T10:48:06.240520</td>\n",
       "      <td>60.064157</td>\n",
       "      <td>20</td>\n",
       "      <td>6 7 7 7 8 0 4 8 11 7 11 3 2 4 4 8 11 4 3 7</td>\n",
       "      <td>0</td>\n",
       "      <td>12359</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>2018-11-03T10:48:06.240604</td>\n",
       "      <td>2018-11-03T10:49:06.314280</td>\n",
       "      <td>60.073676</td>\n",
       "      <td>20</td>\n",
       "      <td>7 11 10 5 2 2 6 11 10 6 8 11 11 3 5 9 0 11 2 7</td>\n",
       "      <td>0</td>\n",
       "      <td>12520</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>2018-11-03T10:49:07.549236</td>\n",
       "      <td>2018-11-03T10:50:07.584409</td>\n",
       "      <td>60.035173</td>\n",
       "      <td>20</td>\n",
       "      <td>5 8 6 11 8 11 9 5 3 6 6 11 8 11 8 5 5 10 9 9</td>\n",
       "      <td>0</td>\n",
       "      <td>6198</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>169 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       start_dt                     stop_dt   duration  depth  \\\n",
       "70   2018-11-03T07:28:27.937404  2018-11-03T07:29:27.965407  60.028003      4   \n",
       "80   2018-11-03T07:29:41.432092  2018-11-03T07:30:41.465964  60.033872      5   \n",
       "84   2018-11-03T07:31:44.788987  2018-11-03T07:32:44.825293  60.036306      5   \n",
       "90   2018-11-03T07:32:46.300011  2018-11-03T07:33:46.318301  60.018290      5   \n",
       "94   2018-11-03T07:33:47.996212  2018-11-03T07:34:48.031956  60.035744      5   \n",
       "106  2018-11-03T07:35:23.475751  2018-11-03T07:36:23.491487  60.015736      6   \n",
       "114  2018-11-03T07:37:27.455060  2018-11-03T07:38:27.543656  60.088596      6   \n",
       "123  2018-11-03T07:39:32.422113  2018-11-03T07:40:32.458444  60.036331      7   \n",
       "125  2018-11-03T07:40:36.349231  2018-11-03T07:41:36.376140  60.026909      7   \n",
       "131  2018-11-03T07:41:51.924902  2018-11-03T07:42:52.005646  60.080744      7   \n",
       "133  2018-11-03T07:43:19.376918  2018-11-03T07:44:19.414716  60.037798      7   \n",
       "141  2018-11-03T07:44:24.980411  2018-11-03T07:45:25.019995  60.039584      8   \n",
       "145  2018-11-03T07:45:34.331774  2018-11-03T07:46:34.388275  60.056501      8   \n",
       "146  2018-11-03T07:46:34.388370  2018-11-03T07:47:34.486795  60.098425      8   \n",
       "147  2018-11-03T07:47:34.496008  2018-11-03T07:48:34.526353  60.030345      8   \n",
       "148  2018-11-03T07:48:34.526447  2018-11-03T07:49:34.570087  60.043640      8   \n",
       "149  2018-11-03T07:49:34.570239  2018-11-03T07:50:34.607524  60.037285      8   \n",
       "150  2018-11-03T07:50:34.607674  2018-11-03T07:51:34.660917  60.053243      8   \n",
       "155  2018-11-03T07:52:07.618923  2018-11-03T07:53:07.652775  60.033852      8   \n",
       "156  2018-11-03T07:53:07.652922  2018-11-03T07:54:07.670170  60.017248      8   \n",
       "157  2018-11-03T07:54:07.670324  2018-11-03T07:55:07.733730  60.063406      8   \n",
       "158  2018-11-03T07:55:07.733825  2018-11-03T07:56:07.757373  60.023548      8   \n",
       "159  2018-11-03T07:56:07.757523  2018-11-03T07:57:07.833445  60.075922      8   \n",
       "161  2018-11-03T07:57:13.646697  2018-11-03T07:58:13.698956  60.052259      9   \n",
       "163  2018-11-03T07:58:32.027507  2018-11-03T07:59:32.134805  60.107298      9   \n",
       "164  2018-11-03T07:59:32.134898  2018-11-03T08:00:32.169663  60.034765      9   \n",
       "165  2018-11-03T08:00:32.169809  2018-11-03T08:01:32.273574  60.103765      9   \n",
       "166  2018-11-03T08:01:32.273671  2018-11-03T08:02:32.371686  60.098015      9   \n",
       "167  2018-11-03T08:02:32.371780  2018-11-03T08:03:32.392502  60.020722      9   \n",
       "168  2018-11-03T08:03:32.392652  2018-11-03T08:04:32.452510  60.059858      9   \n",
       "..                          ...                         ...        ...    ...   \n",
       "359  2018-11-03T10:17:59.527796  2018-11-03T10:18:59.587762  60.059966     18   \n",
       "361  2018-11-03T10:18:59.657119  2018-11-03T10:19:59.707851  60.050732     19   \n",
       "362  2018-11-03T10:19:59.707995  2018-11-03T10:20:59.753776  60.045781     19   \n",
       "363  2018-11-03T10:20:59.753916  2018-11-03T10:21:59.791365  60.037449     19   \n",
       "364  2018-11-03T10:21:59.791506  2018-11-03T10:22:59.839420  60.047914     19   \n",
       "365  2018-11-03T10:22:59.839563  2018-11-03T10:23:59.867941  60.028378     19   \n",
       "366  2018-11-03T10:23:59.868091  2018-11-03T10:24:59.941758  60.073667     19   \n",
       "368  2018-11-03T10:25:03.972281  2018-11-03T10:26:03.995328  60.023047     19   \n",
       "369  2018-11-03T10:26:03.995468  2018-11-03T10:27:04.016416  60.020948     19   \n",
       "370  2018-11-03T10:27:04.016551  2018-11-03T10:28:04.067421  60.050870     19   \n",
       "371  2018-11-03T10:28:04.067581  2018-11-03T10:29:04.104962  60.037381     19   \n",
       "372  2018-11-03T10:29:04.105105  2018-11-03T10:30:04.137153  60.032048     19   \n",
       "375  2018-11-03T10:31:22.442663  2018-11-03T10:32:22.479913  60.037250     19   \n",
       "376  2018-11-03T10:32:22.480025  2018-11-03T10:33:22.511064  60.031039     19   \n",
       "377  2018-11-03T10:33:22.511204  2018-11-03T10:34:22.552686  60.041482     19   \n",
       "378  2018-11-03T10:34:22.552817  2018-11-03T10:35:22.608276  60.055459     19   \n",
       "380  2018-11-03T10:35:31.497771  2018-11-03T10:36:31.525108  60.027337     20   \n",
       "381  2018-11-03T10:36:31.525229  2018-11-03T10:37:31.559053  60.033824     20   \n",
       "382  2018-11-03T10:37:31.559192  2018-11-03T10:38:31.621067  60.061875     20   \n",
       "383  2018-11-03T10:38:31.621207  2018-11-03T10:39:31.666771  60.045564     20   \n",
       "384  2018-11-03T10:39:31.666912  2018-11-03T10:40:31.704211  60.037299     20   \n",
       "385  2018-11-03T10:40:31.704350  2018-11-03T10:41:31.746074  60.041724     20   \n",
       "386  2018-11-03T10:41:31.746213  2018-11-03T10:42:31.776821  60.030608     20   \n",
       "387  2018-11-03T10:42:31.776909  2018-11-03T10:43:31.816193  60.039284     20   \n",
       "388  2018-11-03T10:43:31.816280  2018-11-03T10:44:31.851691  60.035411     20   \n",
       "389  2018-11-03T10:44:31.851775  2018-11-03T10:45:31.889726  60.037951     20   \n",
       "391  2018-11-03T10:46:06.138408  2018-11-03T10:47:06.176277  60.037869     20   \n",
       "392  2018-11-03T10:47:06.176363  2018-11-03T10:48:06.240520  60.064157     20   \n",
       "393  2018-11-03T10:48:06.240604  2018-11-03T10:49:06.314280  60.073676     20   \n",
       "395  2018-11-03T10:49:07.549236  2018-11-03T10:50:07.584409  60.035173     20   \n",
       "\n",
       "                                           scramble  is_solved  solve_steps  \\\n",
       "70                                         2 10 1 4          0         4370   \n",
       "80                                       7 5 9 0 11          0         5679   \n",
       "84                                       5 5 9 11 6          0         4156   \n",
       "90                                      1 1 10 11 6          0         3547   \n",
       "94                                        2 4 4 2 1          0         3308   \n",
       "106                                   11 10 9 5 7 8          0         3640   \n",
       "114                                    4 9 0 5 3 10          0         4584   \n",
       "123                                 1 0 10 2 6 3 10          0         4292   \n",
       "125                                   2 3 4 2 6 4 4          0         2922   \n",
       "131                                  8 4 8 1 8 10 7          0         3467   \n",
       "133                                   5 3 3 7 6 8 4          0         3962   \n",
       "141                                5 7 7 9 9 1 1 10          0         3375   \n",
       "145                              7 3 6 10 6 3 10 11          0         3532   \n",
       "146                                 9 0 3 0 8 1 9 6          0         6218   \n",
       "147                                3 8 11 7 6 2 1 5          0         2677   \n",
       "148                              11 2 0 9 5 10 9 10          0         4772   \n",
       "149                                3 5 1 6 2 6 9 11          0         5521   \n",
       "150                               2 6 7 0 10 2 10 7          0         3314   \n",
       "155                                7 5 6 10 6 5 7 3          0         7066   \n",
       "156                                6 4 5 2 6 10 5 8          0         2474   \n",
       "157                               4 7 8 7 0 7 10 10          0         5105   \n",
       "158                                 0 4 8 5 1 1 9 9          0         5448   \n",
       "159                                 9 0 0 4 9 6 3 0          0         6369   \n",
       "161                           3 0 11 3 4 11 11 7 11          0         4974   \n",
       "163                            1 5 4 2 11 9 11 10 9          0         8715   \n",
       "164                             2 3 4 1 0 10 11 2 4          0         7278   \n",
       "165                              11 1 2 2 0 9 4 3 4          0        13498   \n",
       "166                            10 2 1 10 3 1 11 9 1          0         3751   \n",
       "167                             9 4 4 7 4 0 1 10 11          0         4618   \n",
       "168                            9 7 3 11 3 11 10 2 4          0         5656   \n",
       "..                                              ...        ...          ...   \n",
       "359          1 9 4 4 9 11 8 4 7 3 10 1 11 4 0 2 4 6          0         7329   \n",
       "361        9 10 1 3 1 9 9 5 7 3 1 11 8 1 4 3 1 10 6          0         9645   \n",
       "362        6 5 7 4 11 7 0 4 7 11 3 11 8 5 1 6 4 7 4          0         5430   \n",
       "363      8 4 1 11 10 9 5 2 11 3 7 3 11 0 0 5 1 9 10          0         5389   \n",
       "364          9 2 5 5 10 6 6 3 1 2 1 9 6 4 8 3 5 3 1          0         3349   \n",
       "365         9 11 7 9 6 4 5 6 10 0 0 2 2 1 1 9 4 1 3          0         5281   \n",
       "366        4 11 0 5 5 10 5 8 7 5 0 5 1 8 4 7 11 4 8          0         7189   \n",
       "368       2 0 8 10 3 4 1 5 10 3 10 6 11 8 5 3 6 5 4          0         4411   \n",
       "369      1 6 10 8 7 6 11 10 11 10 0 0 2 6 5 8 7 6 2          0         4661   \n",
       "370         11 6 10 9 5 2 1 1 9 1 4 9 6 3 0 0 5 8 6          0         3937   \n",
       "371        5 6 3 2 3 4 8 5 8 5 1 11 11 4 9 2 6 11 2          0         3249   \n",
       "372        11 0 11 0 1 1 6 1 9 1 3 0 3 11 4 6 2 2 7          0         6841   \n",
       "375       0 0 4 11 10 0 1 1 11 6 3 7 0 9 8 9 0 5 10          0         3146   \n",
       "376          9 6 6 6 6 2 1 9 1 8 1 2 3 8 1 2 3 8 10          0         5982   \n",
       "377          5 1 3 4 3 6 2 2 4 1 1 6 5 5 4 1 6 1 11          0         8593   \n",
       "378       5 0 10 3 10 5 8 7 4 5 1 4 1 10 9 9 11 8 5          0         7290   \n",
       "380      8 1 3 7 8 3 0 7 8 0 3 4 0 10 10 11 7 9 4 4          0         4787   \n",
       "381     9 11 8 10 6 4 0 2 4 11 11 4 2 7 0 9 4 1 8 4          0         6473   \n",
       "382       2 9 5 6 7 9 2 2 10 7 9 5 4 4 6 8 11 6 4 5          0        11773   \n",
       "383   10 3 1 11 10 7 2 10 2 4 4 8 6 3 11 9 9 7 10 1          0         8750   \n",
       "384       7 2 7 3 7 9 9 11 2 5 2 4 5 6 7 11 3 8 4 1          0         5905   \n",
       "385      5 4 5 9 0 1 1 10 7 11 0 2 5 6 2 6 11 2 0 9          0         6909   \n",
       "386       9 1 6 9 11 8 9 5 0 9 4 1 2 2 3 7 9 11 4 4          0         3010   \n",
       "387   4 5 7 8 7 11 11 11 4 11 6 8 9 0 2 1 11 6 7 10          0         8145   \n",
       "388    0 11 0 0 8 3 5 1 9 1 10 11 3 3 5 5 10 0 7 11          0         5160   \n",
       "389    6 3 0 3 1 9 6 6 10 5 2 7 4 11 11 11 7 6 11 3          0         7564   \n",
       "391    8 3 1 11 10 11 10 2 3 8 11 1 6 6 1 1 5 5 1 8          0         6524   \n",
       "392      6 7 7 7 8 0 4 8 11 7 11 3 2 4 4 8 11 4 3 7          0        12359   \n",
       "393  7 11 10 5 2 2 6 11 10 6 8 11 11 3 5 9 0 11 2 7          0        12520   \n",
       "395    5 8 6 11 8 11 9 5 3 6 6 11 8 11 8 5 5 10 9 9          0         6198   \n",
       "\n",
       "     solution  \n",
       "70        NaN  \n",
       "80        NaN  \n",
       "84        NaN  \n",
       "90        NaN  \n",
       "94        NaN  \n",
       "106       NaN  \n",
       "114       NaN  \n",
       "123       NaN  \n",
       "125       NaN  \n",
       "131       NaN  \n",
       "133       NaN  \n",
       "141       NaN  \n",
       "145       NaN  \n",
       "146       NaN  \n",
       "147       NaN  \n",
       "148       NaN  \n",
       "149       NaN  \n",
       "150       NaN  \n",
       "155       NaN  \n",
       "156       NaN  \n",
       "157       NaN  \n",
       "158       NaN  \n",
       "159       NaN  \n",
       "161       NaN  \n",
       "163       NaN  \n",
       "164       NaN  \n",
       "165       NaN  \n",
       "166       NaN  \n",
       "167       NaN  \n",
       "168       NaN  \n",
       "..        ...  \n",
       "359       NaN  \n",
       "361       NaN  \n",
       "362       NaN  \n",
       "363       NaN  \n",
       "364       NaN  \n",
       "365       NaN  \n",
       "366       NaN  \n",
       "368       NaN  \n",
       "369       NaN  \n",
       "370       NaN  \n",
       "371       NaN  \n",
       "372       NaN  \n",
       "375       NaN  \n",
       "376       NaN  \n",
       "377       NaN  \n",
       "378       NaN  \n",
       "380       NaN  \n",
       "381       NaN  \n",
       "382       NaN  \n",
       "383       NaN  \n",
       "384       NaN  \n",
       "385       NaN  \n",
       "386       NaN  \n",
       "387       NaN  \n",
       "388       NaN  \n",
       "389       NaN  \n",
       "391       NaN  \n",
       "392       NaN  \n",
       "393       NaN  \n",
       "395       NaN  \n",
       "\n",
       "[169 rows x 8 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c2_zg_df[c2_zg_df.is_solved == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Error in steps limit, rerun tests"
   ]
  }
 ],
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